Results 1 to 10 of about 183,993 (251)

Fair Canonical Correlation Analysis. [PDF]

Adv Neural Inf Process Syst, 2023
Accepted for publication at NeurIPS 2023, 31 Pages, 14 ...
Zhou Z, Tarzanagh DA, Hou B, Tong B, Xu J, Feng Y, Long Q, Shen L.   +7 more
europepmc   +5 more sources

Longitudinal Canonical Correlation Analysis. [PDF]

J R Stat Soc Ser C Appl Stat, 2023
AbstractThis paper considers canonical correlation analysis for two longitudinal variables that are possibly sampled at different time resolutions with irregular grids. We modelled trajectories of the multivariate variables using random effects and found the most correlated sets of linear combinations in the latent space.
Lee S, Choi J, Fang Z, Bowman FD, Alzheimer’s Disease Neuroimaging Initiative.   +4 more
europepmc   +5 more sources

An Improved Canonical Correlation Analysis for EEG Inter-Band Correlation Extraction [PDF]

Bioengineering, 2023
(1) Background: Emotion recognition based on EEG signals is a rapidly growing and promising research field in affective computing. However, traditional methods have focused on single-channel features that reflect time-domain or frequency-domain ...
Zishan Wang, Ruqiang Huang, Ye Yan, Zhiguo Luo, Shaokai Zhao, Bei Wang, Jing Jin, Liang Xie, Erwei Yin   +8 more
doaj   +2 more sources

Multimodal Brain Growth Patterns: Insights from Canonical Correlation Analysis and Deep Canonical Correlation Analysis with Auto-Encoder [PDF]

Information
Today’s advancements in neuroimaging have been pivotal in enhancing our understanding of brain development and function using various MRI techniques.
Ram Sapkota, Bishal Thapaliya, Bhaskar Ray, Pranav Suresh, Jingyu Liu   +4 more
doaj   +2 more sources

Permutation inference for canonical correlation analysis

NeuroImage, 2020
Canonical correlation analysis (CCA) has become a key tool for population neuroimaging, allowing investigation of associations between many imaging and non-imaging measurements.
Anderson M. Winkler, Olivier Renaud, Stephen M. Smith, Thomas E. Nichols   +3 more
doaj   +5 more sources

Canonical correlation analysis for multi-omics: Application to cross-cohort analysis. [PDF]

PLoS Genetics, 2023
Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest.
Min-Zhi Jiang, François Aguet, Kristin Ardlie, Jiawen Chen, Elaine Cornell, Dan Cruz, Peter Durda, Stacey B Gabriel, Robert E Gerszten, Xiuqing Guo, Craig W Johnson, Silva Kasela, Leslie A Lange, Tuuli Lappalainen, Yongmei Liu, Alex P Reiner, Josh Smith, Tamar Sofer, Kent D Taylor, Russell P Tracy, David J VanDenBerg, James G Wilson, Stephen S Rich, Jerome I Rotter, Michael I Love, Laura M Raffield, Yun Li, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Analysis Working Group   +27 more
doaj   +2 more sources

Structure-adaptive canonical correlation analysis for microbiome multi-omics data [PDF]

Frontiers in Genetics
Sparse canonical correlation analysis (sCCA) has been a useful approach for integrating different high-dimensional datasets by finding a subset of correlated features that explain the most correlation in the data.
Linsui Deng, Yanlin Tang, Xianyang Zhang, Jun Chen   +3 more
doaj   +2 more sources

Chunk Incremental Canonical Correlation Analysis [PDF]

Jisuanji kexue yu tansuo, 2022
For the large-scale dynamic data stream, incremental learning is an effective and efficient technique and is widely used in machine learning. Incremental dimensionality reduction algorithms have been proposed by many scholars.
PAN Yu, CHEN Xiaohong, LI Shunming, LI Jiyong
doaj   +1 more source

Canonical Concordance Correlation Analysis

Mathematics, 2022
A multivariate technique named Canonical Concordance Correlation Analysis (CCCA) is introduced. In contrast to the classical Canonical Correlation Analysis (CCA) which is based on maximization of the Pearson’s correlation coefficient between the linear ...
Stan Lipovetsky
doaj   +1 more source

Sparse canonical correlation analysis [PDF]

Machine Learning, 2010
We present a novel method for solving Canonical Correlation Analysis (CCA) in a sparse convex framework using a least squares approach. The presented method focuses on the scenario when one is interested in (or limited to) a primal representation for the first view while having a dual representation for the second view.
David R. Hardoon, John Shawe-Taylor
openaire   +3 more sources